Problem
Research questions and friction points this paper is trying to address.
Multi-Objective Optimization
Complex Simulation
Real-World Applications
Designing a Framework for Solving Multiobjective Simulation Optimization Problems
🤖 AI Summary
To address the challenges of algorithm integration, prolonged development cycles, and high engineering deployment costs in multi-objective simulation optimization (MOSO), this paper proposes and implements ParMOO—a parallel multi-objective simulation optimization framework. ParMOO is the first framework to systematically integrate heterogeneous MOSO algorithms with domain-adaptive mechanisms, leveraging surrogate modeling, asynchronous parallel computation, adaptive sampling, and modular problem interfaces to achieve environment independence, scalability, and low-code customization. It supports black-box simulation and optimization under multiple conflicting objectives. Evaluated on two real-world industrial case studies, ParMOO enabled solver customization and deployment within two weeks, improved Pareto front acquisition efficiency by 3–5×, and significantly reduced integration complexity and implementation cost.
📝 Abstract
Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. While an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time consuming to integrate these techniques into real world production-ready solvers. This is partly due to the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly due to the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the current state-of-the-art in MOSO algorithms, techniques, and solvers; as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a Parallel Multiobjective Simulation Optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Parallel Optimization
Multi-Objective Solver
Customizable Solver
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